Approach to distributed computing system development for three-dimensional geoelectromagnetic problem solving

i»? A. A. Helal, A. A. Heddaya, and B. B. Bhargrava Kluwer Academic Publishers, Boston, 1996, 176 pp. ISBN 0-7923-9800-9, $118.50 Computing in the 1990s has reached the state of distributed computing. A basis of this form of computing is a distributed computing system, which is built on the following three components: (a) personal computers, (b) local and fast wide area networks, and (c) system and application software. By amalgamating computers and networks into one single computing system and providing an appropriate system software, a distributed computing system has created the possibility of sharing information and peripheral resources. Furthermore, these systems improved performance of a computing system and individual users through parallel execution of programs, load balancing and sharing, and replication of programs and data. Distributed computing systems are also characterized by enhanced availability, and increased reliability. Replication is the key to providing high availability, fault tolerance, and enhanced performance in a distributed computing system. As companies move toward systems that are more open and distributed, replication is becoming increasingly important in the ability to provide data and services that are current, correct and available, which is a key factor in maintaining a competitive advantage over rivals. However, replication has also generated some serious challenges. For example, when a replica is updated, how do we propagate such an update to other replicas? If multiple replicas are updated simultaneously and some of the updates conflict one another, how do we resolve the conflict? How do we deal with the situation where a replica is down and then subsequently recovered? How do we deal with network partitions? Not surprisingly, considerable research efforts have been directed towards the solution of these challenges. However, most of the research results are scattered among many journals, conference proceedings, theses, and technical reports. The book by Helal, Heddaya and Bhargava has gathered the best material of these research results and formed a coherent collection that includes definitions, theoretical background, algorithms, annotated bibliographies of commercial and experimental prototype systems, and more than 200 references. This book classifies the entities that can be replicated on a distributed computing system into the following categories: (a) data, (b) process, (c) objects, and (d) messages. After the introduction on the goals and main approaches of replication techniques, the book uses individual chapters to describe major methods for each of the above replication categories. The main focus of this part, however, is on the replication of data. The book also contains two chapters on the replication issues in heterogeneous, mobile, and large-scale systems, and on the future of replication techniques. Apart from the normal chapters, the book contains a rich set of appendices that are very useful for further studies of replication techniques. These appendices include brief descriptions of two dozen commercial and experimental systems that use replication techniques, annotated bibliographies of selected literature on various topics of replication, and an introduction on serializability theory. Each annotated bibliography contains an overview of the specific topic and is written by an invited expert in that field. The combination of the normal chapters and appendices cover the entire spectrum of replication, from definitions, concepts, theories, algorithms, techniques, to systems. The book serves as an excellent introduction and roadmap to the issues of replication. Although it does not provide a detailed description of every replication techniques and systems, the book covers most of the fundamental work that allows the reader to understand the roots of these techniques and systems, and to find the ways to search for the details. This book is a valuable reference book for anyone studying replication techniques or implementing replication systems, specifically, it can be very useful to post-graduate students, practitioners, and beginning researchers of this exciting field. Wanlei Zhou, School of Computing and Mathematics, Deakin University

The integration of the Internet of Things (IoT) and Distributed Computing Systems (DCS) is transforming business models across industries. IoT devices allow immediate monitoring of equipment and processes, mitigating lost time and enhancing efficiency. In this case, manufacturing companies use IoT sensors to monitor machinery, predict failures, and schedule maintenance. Also, automation via IoT reduces manual intervention, resulting in boosted productivity in smart factories and automated supply chains. IoT devices generate this vast amount of data, which businesses analyze to gain insights into customer behavior, operational inefficiencies, and market trends. In turn, Distributed Computing Systems process this data, providing actionable insights and enabling advanced analytics and machine learning for future trend predictions. While, IoT facilitates personalized products and services by collecting data on customer preferences and usage patterns, enhancing satisfaction and loyalty, IoT devices support new customer interactions, like wearable health devices, and enable subscription-based and pay-per-use models in transportation and utilities. Conversely, real-time monitoring enhances security, as distributed systems quickly respond to threats, ensuring operational safety. It also aids regulatory compliance by providing accurate operational data. In this way, this study, through a Bibliometric Literature Review (LRSB) of 91 screened pieces of literature, aims at ascertaining to what extent the aforementioned capacities, overall, enhance business models, in terms of efficiency and effectiveness. The study concludes that those systems altogether leverage businesses, promoting competitive edge, continuous innovation, and adaptability to market dynamics. In particular, overall, the integration of both IoT and Distributed Systems in business models augments its numerous advantages: it develops smart infrastructures e.g., smart grids; edge computing that allows data processing closer to the data source e.g., autonomous vehicles; predictive analytics, by helping businesses anticipate issues e.g., to foresee equipment failures; personalized services e.g., through e-commerce platforms of personalized recommendations to users; enhanced security, while reducing the risk of centralized attacks e.g., blockchain technology, in how IoT and Distributed Computing Systems altogether impact business models. Future research avenues are suggested.

The MARUTI operating system is designed to support hard real-time applications on distributed computer systems while providing a fault-tolerant operation. Its design is object oriented, and the communication mechanism allows transparent use of the resources of a distributed system. Fault tolerance is provided through a consistent set of mechanisms that support a number of policies. Most important, MARUTI supports guaranteed-service scheduling, by which jobs that are accepted by the system are guaranteed to meet the time constraints of the computation requests with a specified degree of fault tolerance. As a consequence, MARUTI applications can be executed in a predictable fashion. The development of current hard real-time applications requires that the analyst estimate the resource requirements for all parts of the computation and then makes sure that the resources are available to meet the time constraints, which tends to be a cumbersome process. As a part of the MARUTI system, a set of tools which support the hard real-time applications during various phases of their life cycle has been developed. The present version of MARUTI has been implemented as a prototype running on a Unix platform. Experiences with the development of this prototype are also presented. >

A performance analysis of a buddy system for fault tolerance

Dealing with large volumes of data is resource-consuming work which is more and more often delegated not only to a single computer but also to a whole distributed computing system at once. As the number of computers in a distributed system increases, the amount of effort put into effective management of the system grows. When the system reaches some critical size, much effort should be put into improving its fault tolerance. It is difficult to estimate when some particular distributed system needs such facilities for a given workload, so instead they should be implemented in a middleware which works efficiently with a distributed system of any size. It is also difficult to estimate whether a volume of data is large or not, so the middleware should also work with data of any volume. In other words, the purpose of the middleware is to provide facilities that adapt distributed computing system for a given workload. In this paper we introduce such middleware appliance. Tests show that this middleware is well-suited for typical HPC and big data workloads and its performance is comparable with well-known alternatives.

Nowadays developing methods for distributed computing is received much attention. One of the methods of distributed computing is using of multi-agent systems. The organization of distributed computing based on the conventional network computers can experience security threats performed by computational processes. Authors have developed the unified agent algorithm of control system of computing network nodes operation. Network PCs is used as computing nodes. The proposed multi-agent control system for the implementation of distributed computing allows in a short time to organize using of the processing power of computers any existing network to solve large-task by creating a distributed computing. Agents based on a computer network can: configure a distributed computing system; to distribute the computational load among computers operated agents; perform optimization distributed computing system according to the computing power of computers on the network. The number of computers connected to the network can be increased by connecting computers to the new computer system, which leads to an increase in overall processing power. Adding multi-agent system in the central agent increases the security of distributed computing. This organization of the distributed computing system reduces the problem solving time and increase fault tolerance (vitality) of computing processes in a changing computing environment (dynamic change of the number of computers on the network). Developed a multi-agent system detects cases of falsification of the results of a distributed system, which may lead to wrong decisions. In addition, the system checks and corrects wrong results.

This paper addresses the problem of static load balancing in heterogeneous distributed computing systems taking into account both memory and communication capacity constraints. It first models the load balancing problem as an optimization problem. It then presents a modified genetic algorithm, called Adaptive Genetic Algorithm (AGA), to solve the problem. The performance of the proposed algorithm is evaluated by simulation studies on randomly generated instances and the results are compared with that obtained by applying both the Genetic Algorithm (GA) and the Simulated Annealing (SA). Also, the qualities of the results are compared with the optimal solutions that obtained by applying the Brach-and-Bound (BB) algorithm.

This paper proposes a reliable routing protocol called LBR for wireless sensor networks, which find high-quality paths with considering load balance among nodes. The LBR protocol transmits messages through a reliable path to reduce the packet loss rate. To the problem of increasing data transmission reliability, our routing protocol uses ETX as a metric for estimating link quality. By using ETX, the LBR protocol can choose good links in the network. In addition to the above, the problem of load balance is also considered in our protocol. By taking this into account, the LBR protocol avoid routing through the congested nodes and balance load among nodes better. This results in increasing the lifetime of the network. The simulation results using the Qualnet simulator showed that the proposed protocol outperforms other protocols in terms of packet delivery ratio, end-to-end delay and the efficiency of load balance.KeywordsWireless sensor networksload balancelink qualityrouting protocol

Aiming at the problem of load balancing and lifetime prolonging for wireless sensor networks (WSNs), and considering complex uncertainties existed in WSNs, this paper proposes a clustering routing protocol CRT2FLACO for WSN based on type-2 fuzzy logic and ant colony optimization (ACO). Specifically, in the cluster set-up phase, a type-2 Mamdnai fuzzy logic system (T2MFLS) is built to handle rule uncertainty better and balance the network load, in which three important factors - residual energy, the number of neighbor nodes and the distance to the base station (BS) of a node - are considered as inputs, and the probability of the node to be a candidate cluster head (CH) and the CH competition radius as outputs of our T2MFLS, to select the final CHs; in the steady-state phase, in order to reduce the transmission consumption, all the CHs are linked into a chain using ACO algorithm, then each CH send its data packet to the leader along link, which is a CH eventually transmitting packets to the BS. The simulation results show that the proposed routing protocol can effectively balance network load and reduce the transmission energy consumption of CHs, thus greatly prolong the lifetime of WSN.

In heterogeneous networks (HetNets), user association is one of the most significant tools to deal with the problem of load balancing or interference mitigation. However, though much work has been down on the design of user association scheme, some important real-world policies are largely ignored and fail to be considered simultaneously. In this paper, we investigate the user association scheme in HetNets with the rules such as traffic capacity constraint, power budget, stable connection guarantee and transmission cost-optimal. Firstly we formulate a multi-objective optimization problem with users of different quality of service (QoS) requirements (constant bit rate requirement (CBR) users and best effort (BE) users). The objectives of the problem are balancing the load distribution of CBR users, maximizing the throughput of BE users and minimizing network transmission cost, respectively. Then we analyze how to optimize the three objectives jointly and employ binary particle swarm optimizer to solve the problem. Extensive simulations are conducted, the results show that our algorithm can lead to significantly better performances than traditional algorithms, i.e., higher balancing index for CBR users, higher throughput for BE users, low transmission cost and more stable link connection.

This paper reports on the results of a preliminary study in dynamic load balancing on an Intel Hypercube. The purpose of this research is to provide experimental data in how parallel algorithms should be constructed to obtain maximal utilization of a parallel architecture. This study is one aspect of an ongoing research project into the construction of an automated parallelization tool. This tool will take FORTRAN source as input, and construct a parallel algorithm that will produce the same results as the original serial input. The focus of this paper is on the load balancing aspect of that project. The basic idea is to reserve a certain percentage of the computation task, subdivide that percentage into arbitrarily fine tasks, and dole those small tasks out to nodes on request. Ij” the percentage is chosen correctly, then a minority of nodes should be involved in consuming the filler tasks, and the overall throughput of the job should increase as a result of the individual node efJciencies having increased. This paper will outline our approach to performing dynamic load balancing on an Intel iPSC/2. We take the view that the problem of load balancing is really a problem of dividing a “computational task” into smaller components, each of roughly equal complexity, and each an independent event. After this is done, the components of the task can be sent to a node for execution. The key to an optimally balanced load across all computational nodes is the ability to form a statistical profile of the individual components of each computational task. This statistical profile will determine an initial sequence of execution. Our experience indicates that a speedup on the order of 80% is achievable with the judicious use of profiled load balancing. During the process of execution, the initial profile will be altered according to the actual behavior exhibited by the nodes. The difference between the actual and expected performance will be used to determine how much additional time should be devoted to altering the current execution schedule. Currently, our work involves statically setting the load balancing parameters. Our load balancing system determines the execution schedule

In order to build large-scale RFID applications, it is necessary to establish public RFID service infrastructure, the essential task is to build efficient and secure RFID code resolution network. Aiming at alleviating the problems in load balancing, single node failure tolerance and security of resolution network, based on related standards and research works, a hierarchical P2P based RFID code resolution network structure is proposed, k-Nearest Neighbor storing and hierarchical querying mechanism are presented in static resolution, secondary indexing and hierarchical querying mechanism are presented in dynamic resolution, double random number based entity and message authentication mechanism is given for the correctness of resolution service. Based on modified Kademlia, RFID code resolution services and related security mechanism are implemented. In experiments, the availability, load balancing and single node failure tolerance of our resolution network are verified.

Scheduling for speculative parallelization is a problem that remained unsolved despite its importance [2]. In the previous work scheduling was done based on Fixed-Size Chunking (FSC) technique which needed several 'dry-runs' before an acceptable finalized chunk size that will be scheduled to each processors is found. There are many other scheduling methods which were originally designed for loops with no dependences, but they were primarily focused in the problem of load balancing. In this work we address the problem of scheduling tasks with and without dependences for speculative execution. We have found that a complexity between minimizing the number of re-executions and reducing overheads can be found if the size of the scheduled block of iterations is calculated at runtime. We introduce here a scheduling method called Parallelization of Resource scheduling (PRS) in which we first analyze the processing speed of each worker based on that further division of the actual task will be done. The result shows a 5% to 10% speedup improvement in real applications with dependences with respect to a carefully tuned PRS strategy.

In the paper a multi-agent network system of different computing nodes is considered. A problem of load balancing in the network is addressed. The problem is formulated as consensus achievement problem and solved via local voting protocol. Agents exchange information about their states in presence of noise in communication channels. At certain moment network system topology changes and new step size of control protocol is chosen to meet new conditions. Step size adjustment is done by stochastic approximation type algorithm. Analytically obtained optimal step size values are given. Simulation example demonstrating step size adjustment is provided.

: The problems of load balancing and locality of reference for large distributed memory message passing multiprocessors running object-oriented applications are studied. The object-oriented nature of the applications implies that tasks must be executed on the processor containing the host object for the task (tasks are method executions, the host object is the destination of the message that invokes the method). The particular constraint for the study is that objects will not be moved between processors once allocated. The primary resource allocation problem becomes the determination of the best processor on which to allocate each new object during the execution of the application. The study uses discrete event simulation of pseudo-applications under the control of several resource allocation policies selected as limiting cases of practical solutions. Results are presented, and some general rules-of-thumb are suggested for use in real systems.